DFC-RPs

allrp <- normc$ext_gene[grep("Rp.*", normc$ext_gene)]
dfcrp <- dfcs_w$ext_gene[grep("Rp.*", dfcs_w$ext_gene)]
rpe <-  normc %>% filter(ext_gene %in% allrp) %>% 
  tibble::column_to_rownames("ext_gene") %>%
  t %>% as_tibble(rownames="cell")
rp <- left_join(Df,rpe,by="cell") %>% gather(-(1:ncol(Df)),key=gene,value=expression)

print(rpe)

print(rp)

M <- rp %>% filter(gene %in% dfcrp) %>% dplyr::group_by(cluster,gene) %>% dplyr::summarise(s=mean(expression))

`summarise()` has grouped output by 'cluster'. You can override using the `.groups` argument.

mm <- M %>% tidyr::pivot_wider(c(gene,cluster,s),names_from=cluster,values_from=s) %>%
  tibble::column_to_rownames("gene")
hmrp <- pheatmap::pheatmap(t(mm),scale="none",color=viridis::viridis(21),border_color = NA)

p1 <- rpe[,colnames(rpe)%in%dfcrp] %>% prcomp()
pca1 <- ggbiplot::ggbiplot(pc,group=Df$Target,scale=0.2,alpha=0.6)+ #xlim(-3,5) +
  theme(legend.position="top")+theme_linedraw()
print(pca1)

p2 <- rpe %>% select(-cell) %>% prcomp()
pca2 <- ggbiplot::ggbiplot(p2,group=Df$Target,scale=0.2,alpha=0.6)+ #xlim(-3,5) +
  theme(legend.position="top")+theme_linedraw()
print(pca2)

matd <- rp %>% filter(cluster==12) %>%
  tidyr::pivot_wider(c(cell,gene,expression),names_from=gene,values_from=expression,values_fill=0) %>%
  tibble::column_to_rownames("cell")
matd_ <- matd[,apply(matd, 2, var, na.rm=TRUE) != 0]
pd3 <- prcomp(matd_)
pca3 <- ggbiplot::ggbiplot(pd3,group=Df$injury[lab.POI],scale=0.2,alpha=0.6)+ #xlim(-3,5) +
  theme(legend.position="top")+theme_linedraw()+discrete_color

print(pca3)

matd <- rp %>% filter(gene %in% dfcrp,cluster==12) %>%
  tidyr::pivot_wider(c(cell,gene,expression),names_from=gene,values_from=expression,values_fill=0) %>%
  tibble::column_to_rownames("cell")
matd_ <- matd[,apply(matd, 2, var, na.rm=TRUE) != 0]
pd4 <- prcomp(matd_)
pca4 <- ggbiplot::ggbiplot(pd4,group=Df$injury[lab.POI],scale=0.2,alpha=0.6)+ #xlim(-3,5) +
  theme(legend.position="top")+theme_linedraw()+discrete_color
print(pca4)